Electron-nucleus cross sections from transfer learning
Krzysztof M. Graczyk, Beata E. Kowal, Artur M. Ankowski, Rwik Dharmapal Banerjee, Jose Luis Bonilla, Hemant Prasad, Jan T. Sobczyk
传输学习(TL)允许对一种类型的数据进行训练的深度神经网络(DNN),以适应信息有限的新问题。 我们建议在物理学中使用TL技术。 DNN学习一个过程的细节,经过微调后,它会对相关过程进行预测。 我们考虑了DNN,经过包容性电子碳散射数据的训练,并表明经过微调后,它们准确地预测了电子与从氦-3到铁的核目标相互作用的横截面。
Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets rangi...